{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 関連ライブラリインストール"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ubuntu日本語フォント(Takao)\n",
    "\n",
    "* 日本語が化けるので\n",
    "* コマンド\n",
    "    * apt-get install 'fonts-takao-*'\n",
    "* 確認\n",
    "    * fc-list | grep Takao\n",
    "* 初期化\n",
    "    * ~/.cache/matplotlib/fontList.json を削除\n",
    "    * Jupyterを再起動"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 初期設定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# http://qiita.com/hik0107/items/de5785f680096df93efa\n",
    "# グラフ化に必要なものの準備\n",
    "import matplotlib\n",
    "from matplotlib.font_manager import FontProperties\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Jupyter上に図を表示するためのおまじない\n",
    "%matplotlib inline\n",
    "\n",
    "# データの扱いに必要なライブラリ\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "\n",
    "# 文字サイズを大きくする(デフォルトは12)\n",
    "plt.rcParams[\"font.size\"] = 18\n",
    "\n",
    "# チャートがきれいになるおまじない\n",
    "plt.style.use('ggplot') \n",
    "\n",
    "# 日本語フォント対応\n",
    "font_path = '/usr/share/fonts/truetype/takao-gothic/TakaoPGothic.ttf'\n",
    "font_prop = FontProperties(fname=font_path)\n",
    "matplotlib.rcParams['font.family'] = font_prop.get_name()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## サンプルデータ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>ruby</th>\n",
       "      <th>php</th>\n",
       "      <th>python</th>\n",
       "      <th>perl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>100</td>\n",
       "      <td>40</td>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>60</td>\n",
       "      <td>90</td>\n",
       "      <td>80</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>90</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>80</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name  ruby  php  python  perl\n",
       "0    A   100   40      70    80\n",
       "1    B    60   90      80    10\n",
       "2    C    90   60      60    60\n",
       "3    D    80   70      70    80"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('csv/ruby_1.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 基本的な描画"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 一つの画面に表示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ffde873ba20>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 図を保存"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 初期化 (繰り返し作業をする場合、前の結果が残っている場合があるため)\n",
    "plt.figure()\n",
    "\n",
    "# 図の作成\n",
    "df.plot()\n",
    "\n",
    "# ファイルの保存\n",
    "plt.savefig('img/hoge.png')\n",
    "\n",
    "# 閉じる\n",
    "plt.close('all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### それぞれの画面に表示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f9611c29630>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f9611c38358>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f9611c5e748>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f9611bb8c50>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(subplots=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 行数、列数を指定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f9611a63400>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9611a81b70>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f9611a200f0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9611a385f8>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## タプルで指定\n",
    "df.plot(subplots=True, layout=(2, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### y軸、x軸の範囲を共通化\n",
    "\n",
    "* sharex : x軸の共通化\n",
    "* sharey : y軸の共通化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f9611b0dac8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9611975898>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f961198ec88>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9611926eb8>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## タプルで指定\n",
    "df.plot(subplots=True, layout=(2, 2), sharex=True, sharey=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 棒グラフ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ruby</th>\n",
       "      <th>php</th>\n",
       "      <th>python</th>\n",
       "      <th>perl</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>100</td>\n",
       "      <td>40</td>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>60</td>\n",
       "      <td>90</td>\n",
       "      <td>80</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>90</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>80</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ruby  php  python  perl\n",
       "name                         \n",
       "A      100   40      70    80\n",
       "B       60   90      80    10\n",
       "C       90   60      60    60\n",
       "D       80   70      70    80"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('csv/ruby_1.csv', index_col=0)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f96116491d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f96115c4080>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f96115dc400>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f96115747b8>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.bar(subplots=True, layout=(2, 2), sharex=True, sharey=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 時系列データ\n",
    "\n",
    "* parse_dates=True : データ中の日付文字列をパースして日付型に変換してロード\n",
    "* index_col=0 : 日付データの列をindexに指定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-02-03</th>\n",
       "      <td>83</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-04</th>\n",
       "      <td>80</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-05</th>\n",
       "      <td>85</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-06</th>\n",
       "      <td>72</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-07</th>\n",
       "      <td>81</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             A    B\n",
       "date               \n",
       "2018-02-03  83  140\n",
       "2018-02-04  80  150\n",
       "2018-02-05  85  149\n",
       "2018-02-06  72  142\n",
       "2018-02-07  81  151"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('csv/date.csv', parse_dates=True, index_col=0)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* skiprows=1 : 1行目(date A B)を読み飛ばす\n",
    "* names : 列名を指定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>体重</th>\n",
       "      <th>身長</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日付</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-02-03</th>\n",
       "      <td>83</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-04</th>\n",
       "      <td>80</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-05</th>\n",
       "      <td>85</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-06</th>\n",
       "      <td>72</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-07</th>\n",
       "      <td>81</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            体重   身長\n",
       "日付                 \n",
       "2018-02-03  83  140\n",
       "2018-02-04  80  150\n",
       "2018-02-05  85  149\n",
       "2018-02-06  72  142\n",
       "2018-02-07  81  151"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('csv/date.csv', parse_dates=True, skiprows=1, index_col=0, names=('日付', '体重', '身長'))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fedf503c8d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fedf5059ba8>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#df.plot()\n",
    "df.plot(subplots=True, layout=(1, 2), )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fedf711ee10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fedf50d0d30>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.bar(subplots=True, layout=(1, 2), )"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
